LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 7 of total 7

Search options

  1. Article ; Online: Educational Overview of the Concept and Application of Computer Vision in Arthroplasty.

    Vera-Garcia, Diana V / Nugen, Fred / Padash, Sirwa / Khosravi, Bardia / Mickley, John P / Erickson, Bradley J / Wyles, Cody C / Taunton, Michael J

    The Journal of arthroplasty

    2023  Volume 38, Issue 10, Page(s) 1954–1958

    Abstract: Image data has grown exponentially as systems have increased their ability to collect and store it. Unfortunately, there are limits to human resources both in time and knowledge to fully interpret and manage that data. Computer Vision (CV) has grown in ... ...

    Abstract Image data has grown exponentially as systems have increased their ability to collect and store it. Unfortunately, there are limits to human resources both in time and knowledge to fully interpret and manage that data. Computer Vision (CV) has grown in popularity as a discipline for better understanding visual data. Computer Vision has become a powerful tool for imaging analytics in orthopedic surgery, allowing computers to evaluate large volumes of image data with greater nuance than previously possible. Nevertheless, even with the growing number of uses in medicine, literature on the fundamentals of CV and its implementation is mainly oriented toward computer scientists rather than clinicians, rendering CV unapproachable for most orthopedic surgeons as a tool for clinical practice and research. The purpose of this article is to summarize and review the fundamental concepts of CV application for the orthopedic surgeon and musculoskeletal researcher.
    MeSH term(s) Humans ; Arthroplasty ; Computers ; Orthopedic Procedures ; Orthopedics
    Language English
    Publishing date 2023-08-25
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 632770-9
    ISSN 1532-8406 ; 0883-5403
    ISSN (online) 1532-8406
    ISSN 0883-5403
    DOI 10.1016/j.arth.2023.08.046
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 2134: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article ; Online: Application of Natural Language Processing in Total Joint Arthroplasty: Opportunities and Challenges.

    Nugen, Fred / Vera Garcia, Diana V / Sohn, Sunghwan / Mickley, John P / Wyles, Cody C / Erickson, Bradley J / Taunton, Michael J

    The Journal of arthroplasty

    2023  Volume 38, Issue 10, Page(s) 1948–1953

    Abstract: Total joint arthroplasty is becoming one of the most common surgeries within the United States, creating an abundance of analyzable data to improve patient experience and outcomes. Unfortunately, a large majority of this data is concealed in electronic ... ...

    Abstract Total joint arthroplasty is becoming one of the most common surgeries within the United States, creating an abundance of analyzable data to improve patient experience and outcomes. Unfortunately, a large majority of this data is concealed in electronic health records only accessible by manual extraction, which takes extensive time and resources. Natural language processing (NLP), a field within artificial intelligence, may offer a viable alternative to manual extraction. Using NLP, a researcher can analyze written and spoken data and extract data in an organized manner suitable for future research and clinical use. This article will first discuss common subtasks involved in an NLP pipeline, including data preparation, modeling, analysis, and external validation, followed by examples of NLP projects. Challenges and limitations of NLP will be discussed, closing with future directions of NLP projects, including large language models.
    MeSH term(s) Humans ; Natural Language Processing ; Artificial Intelligence ; Arthroplasty ; Language ; Electronic Health Records
    Language English
    Publishing date 2023-08-22
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 632770-9
    ISSN 1532-8406 ; 0883-5403
    ISSN (online) 1532-8406
    ISSN 0883-5403
    DOI 10.1016/j.arth.2023.08.047
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 2134: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article ; Online: An Overview of Machine Learning in Orthopedic Surgery: An Educational Paper.

    Padash, Sirwa / Mickley, John P / Vera Garcia, Diana V / Nugen, Fred / Khosravi, Bardia / Erickson, Bradley J / Wyles, Cody C / Taunton, Michael J

    The Journal of arthroplasty

    2023  Volume 38, Issue 10, Page(s) 1938–1942

    Abstract: The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine ... ...

    Abstract The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on "good" data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models.
    MeSH term(s) Humans ; Artificial Intelligence ; Machine Learning ; Natural Language Processing ; Orthopedics ; Orthopedic Procedures
    Language English
    Publishing date 2023-08-19
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 632770-9
    ISSN 1532-8406 ; 0883-5403
    ISSN (online) 1532-8406
    ISSN 0883-5403
    DOI 10.1016/j.arth.2023.08.043
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 2134: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Article ; Online: Single-cell dissection of the human motor and prefrontal cortices in ALS and FTLD.

    Pineda, S Sebastian / Lee, Hyeseung / Ulloa-Navas, Maria J / Linville, Raleigh M / Garcia, Francisco J / Galani, Kyriakitsa / Engelberg-Cook, Erica / Castanedes, Monica C / Fitzwalter, Brent E / Pregent, Luc J / Gardashli, Mahammad E / DeTure, Michael / Vera-Garcia, Diana V / Hucke, Andre T S / Oskarsson, Bjorn E / Murray, Melissa E / Dickson, Dennis W / Heiman, Myriam / Belzil, Veronique V /
    Kellis, Manolis

    Cell

    2024  Volume 187, Issue 8, Page(s) 1971–1989.e16

    Abstract: Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) share many clinical, pathological, and genetic features, but a detailed understanding of their associated transcriptional alterations across vulnerable cortical cell types ... ...

    Abstract Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) share many clinical, pathological, and genetic features, but a detailed understanding of their associated transcriptional alterations across vulnerable cortical cell types is lacking. Here, we report a high-resolution, comparative single-cell molecular atlas of the human primary motor and dorsolateral prefrontal cortices and their transcriptional alterations in sporadic and familial ALS and FTLD. By integrating transcriptional and genetic information, we identify known and previously unidentified vulnerable populations in cortical layer 5 and show that ALS- and FTLD-implicated motor and spindle neurons possess a virtually indistinguishable molecular identity. We implicate potential disease mechanisms affecting these cell types as well as non-neuronal drivers of pathogenesis. Finally, we show that neuron loss in cortical layer 5 tracks more closely with transcriptional identity rather than cellular morphology and extends beyond previously reported vulnerable cell types.
    MeSH term(s) Animals ; Humans ; Mice ; Amyotrophic Lateral Sclerosis/genetics ; Amyotrophic Lateral Sclerosis/metabolism ; Amyotrophic Lateral Sclerosis/pathology ; Frontotemporal Dementia/genetics ; Frontotemporal Lobar Degeneration/genetics ; Frontotemporal Lobar Degeneration/metabolism ; Frontotemporal Lobar Degeneration/pathology ; Gene Expression Profiling ; Neurons/metabolism ; Prefrontal Cortex/metabolism ; Prefrontal Cortex/pathology ; Single-Cell Gene Expression Analysis
    Language English
    Publishing date 2024-03-22
    Publishing country United States
    Document type Journal Article
    ZDB-ID 187009-9
    ISSN 1097-4172 ; 0092-8674
    ISSN (online) 1097-4172
    ISSN 0092-8674
    DOI 10.1016/j.cell.2024.02.031
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 1167: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 58: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  5. Article ; Online: Mitigating Bias in Radiology Machine Learning: 3. Performance Metrics.

    Faghani, Shahriar / Khosravi, Bardia / Zhang, Kuan / Moassefi, Mana / Jagtap, Jaidip Manikrao / Nugen, Fred / Vahdati, Sanaz / Kuanar, Shiba P / Rassoulinejad-Mousavi, Seyed Moein / Singh, Yashbir / Vera Garcia, Diana V / Rouzrokh, Pouria / Erickson, Bradley J

    Radiology. Artificial intelligence

    2022  Volume 4, Issue 5, Page(s) e220061

    Abstract: The increasing use of machine learning (ML) algorithms in clinical settings raises concerns about bias in ML models. Bias can arise at any step of ML creation, including data handling, model development, and performance evaluation. Potential biases in ... ...

    Abstract The increasing use of machine learning (ML) algorithms in clinical settings raises concerns about bias in ML models. Bias can arise at any step of ML creation, including data handling, model development, and performance evaluation. Potential biases in the ML model can be minimized by implementing these steps correctly. This report focuses on performance evaluation and discusses model fitness, as well as a set of performance evaluation toolboxes: namely, performance metrics, performance interpretation maps, and uncertainty quantification. By discussing the strengths and limitations of each toolbox, our report highlights strategies and considerations to mitigate and detect biases during performance evaluations of radiology artificial intelligence models.
    Language English
    Publishing date 2022-08-24
    Publishing country United States
    Document type Journal Article
    ISSN 2638-6100
    ISSN (online) 2638-6100
    DOI 10.1148/ryai.220061
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Article ; Online: Mitigating Bias in Radiology Machine Learning: 1. Data Handling.

    Rouzrokh, Pouria / Khosravi, Bardia / Faghani, Shahriar / Moassefi, Mana / Vera Garcia, Diana V / Singh, Yashbir / Zhang, Kuan / Conte, Gian Marco / Erickson, Bradley J

    Radiology. Artificial intelligence

    2022  Volume 4, Issue 5, Page(s) e210290

    Abstract: Minimizing bias is critical to adoption and implementation of machine learning (ML) in clinical practice. Systematic mathematical biases produce consistent and reproducible differences between the observed and expected performance of ML systems, ... ...

    Abstract Minimizing bias is critical to adoption and implementation of machine learning (ML) in clinical practice. Systematic mathematical biases produce consistent and reproducible differences between the observed and expected performance of ML systems, resulting in suboptimal performance. Such biases can be traced back to various phases of ML development: data handling, model development, and performance evaluation. This report presents 12 suboptimal practices during data handling of an ML study, explains how those practices can lead to biases, and describes what may be done to mitigate them. Authors employ an arbitrary and simplified framework that splits ML data handling into four steps: data collection, data investigation, data splitting, and feature engineering. Examples from the available research literature are provided. A Google Colaboratory Jupyter notebook includes code examples to demonstrate the suboptimal practices and steps to prevent them.
    Language English
    Publishing date 2022-08-24
    Publishing country United States
    Document type Journal Article
    ISSN 2638-6100
    ISSN (online) 2638-6100
    DOI 10.1148/ryai.210290
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  7. Article ; Online: Applying Deep Learning to Establish a Total Hip Arthroplasty Radiography Registry: A Stepwise Approach.

    Rouzrokh, Pouria / Khosravi, Bardia / Johnson, Quinn J / Faghani, Shahriar / Vera Garcia, Diana V / Erickson, Bradley J / Maradit Kremers, Hilal / Taunton, Michael J / Wyles, Cody C

    The Journal of bone and joint surgery. American volume

    2022  Volume 104, Issue 18, Page(s) 1649–1658

    Abstract: Background: Establishing imaging registries for large patient cohorts is challenging because manual labeling is tedious and relying solely on DICOM (digital imaging and communications in medicine) metadata can result in errors. We endeavored to ... ...

    Abstract Background: Establishing imaging registries for large patient cohorts is challenging because manual labeling is tedious and relying solely on DICOM (digital imaging and communications in medicine) metadata can result in errors. We endeavored to establish an automated hip and pelvic radiography registry of total hip arthroplasty (THA) patients by utilizing deep-learning pipelines. The aims of the study were (1) to utilize these automated pipelines to identify all pelvic and hip radiographs with appropriate annotation of laterality and presence or absence of implants, and (2) to automatically measure acetabular component inclination and version for THA images.
    Methods: We retrospectively retrieved 846,988 hip and pelvic radiography DICOM files from 20,378 patients who underwent primary or revision THA performed at our institution from 2000 to 2020. Metadata for the files were screened followed by extraction of imaging data. Two deep-learning algorithms (an EfficientNetB3 classifier and a YOLOv5 object detector) were developed to automatically determine the radiographic appearance of all files. Additional deep-learning algorithms were utilized to automatically measure the acetabular angles on anteroposterior pelvic and lateral hip radiographs. Algorithm performance was compared with that of human annotators on a random test sample of 5,000 radiographs.
    Results: Deep-learning algorithms enabled appropriate exclusion of 209,332 DICOM files (24.7%) as misclassified non-hip/pelvic radiographs or having corrupted pixel data. The final registry was automatically curated and annotated in <8 hours and included 168,551 anteroposterior pelvic, 176,890 anteroposterior hip, 174,637 lateral hip, and 117,578 oblique hip radiographs. The algorithms achieved 99.9% accuracy, 99.6% precision, 99.5% recall, and a 99.6% F1 score in determining the radiograph appearance.
    Conclusions: We developed a highly accurate series of deep-learning algorithms to rapidly curate and annotate THA patient radiographs. This efficient pipeline can be utilized by other institutions or registries to construct radiography databases for patient care, longitudinal surveillance, and large-scale research. The stepwise approach for establishing a radiography registry can further be utilized as a workflow guide for other anatomic areas.
    Level of evidence: Diagnostic Level IV . See Instructions for Authors for a complete description of levels of evidence.
    MeSH term(s) Acetabulum/surgery ; Arthroplasty, Replacement, Hip/methods ; Deep Learning ; Hip Prosthesis ; Humans ; Radiography ; Registries ; Retrospective Studies
    Language English
    Publishing date 2022-07-21
    Publishing country United States
    Document type Journal Article
    ZDB-ID 220625-0
    ISSN 1535-1386 ; 0021-9355
    ISSN (online) 1535-1386
    ISSN 0021-9355
    DOI 10.2106/JBJS.21.01229
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Uk I Zs.58/1: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 464: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top